Machine Learning for Economists: An Introduction

Machine Learning (henceforth ML) refers to the set of algorithms and computational methods which enable computers to learn patterns from training data without being explicitly programmed to do so. ML uses training data to learn patterns by estimating a mathematical model and making predictions in out of sample based on new or unseen input data. ML has the tremendous capacity to discover complex, flexible and crucially generalisable structure in training data. Conceptually speaking, ML can be thought of as a set of complex function approximation techniques which help us learn the unknown and potentially highly nonlinear mapping between the data and prediction outcomes, outperforming traditional techniques. 1 In this exposition, my aim is to provide a basic and non-technical overview of 2 machine learning and its applications for economists including development economists. For more technical and complete treatments, you may consult Alpaydin (2020) and James, et al. (2013). You may also wish to refer to my four lecture series on machine learning on YouTube https:// www.youtube.com/watch?v=E9dLEAZW3L4 and my GitHub page for detailed and more technical lecture slides https://github.com/sonanmemon/Introductionto-ML-For-Economists

GeoAI-enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data

abstract: Big data that contain geo-referenced attributes have significantly reformed the way that I process and analyze geospatial data. Compared with the expected benefits received in the data-rich environment, more data have not always contributed to more accurate analysis. “Big but valueless” has becoming a critical concern to the community of GIScience and data-driven geography. As a highly-utilized function of GeoAI technique, deep learning models designed for processing geospatial data integrate powerful computing hardware and deep neural networks into various dimensions of geography to effectively discover the representation of data. However, limitations of these deep learning models have also been reported when People may have to spend much time on preparing training data for implementing a deep learning model. The objective of this dissertation research is to promote state-of-the-art deep learning models in discovering the representation, value and hidden knowledge of GIS and remote sensing data, through three research approaches. The first methodological framework aims to unify varied shadow into limited number of patterns, with the convolutional neural network (CNNs)-powered shape classification, multifarious shadow shapes with a limited number of representative shadow patterns for efficient shadow-based building height estimation. The second research focus integrates semantic analysis into a framework of various state-of-the-art CNNs to support human-level understanding of map content. The final research approach of this dissertation focuses on normalizing geospatial domain knowledge to promote the transferability of a CNN’s model to land-use/land-cover classification. This research reports a method designed to discover detailed land-use/land-cover types that might be challenging for a state-of-the-art CNN’s model that previously performed well on land-cover classification only.Dissertation/ThesisDoctoral Dissertation Geography 201

Maintaining privacy for a recommender system diagnosis using blockchain and deep learning.

The healthcare sector has been revolutionized by Blockchain and AI technologies. Artificial intelligence uses algorithms, recommender systems, decision-making abilities, and big data to display a patient's health records using blockchain. Healthcare professionals can make use of Blockchain to display a patient's medical records with a secured medical diagnostic process. Traditionally, data owners have been hesitant to share medical and personal information due to concerns about privacy and trustworthiness. Using Blockchain technology, this paper presents an innovative model for integrating healthcare data sharing into a recommender diagnostic computer system. Using the model, medical records can be secured, controlled, authenticated, and kept confidential. In this paper, researchers propose a framework for using the Ethereum Blockchain and x-rays as a mechanism for access control, establishing hierarchical identities, and using pre-processing and deep learning to diagnose COVID-19. Along with solving the challenges associated with centralized access control systems, this mechanism also ensures data transparency and traceability, which will allow for efficient diagnosis and secure data sharing